This article briefly describes the evolution of orthopaedic oncology as practised in India over the last two decades. Striking the right balance between adequate resection and functional reconstruction with locally available expertise and resources is a challenging task. Techniques practised by Indian surgeons' have garnered global acceptability, both as durable biological options and cost effective practices in a world which faces ever increasing healthcare budgetary constraints and limitations.Hip fractures in elderly are commonly associated with osteoporosis and surgical outcome is influenced by its concurrent management. The purpose of our study is to determine the association between timing of bisphosphonate administration in inter-trochanteric (IT) fractures and fracture healing. https://www.selleckchem.com/Bcl-2.html Patients with IT fractures (aged?50 years) and T-score ? -1.5 [WHO defines osteopenia as T-score between -1 and -2.5, and osteoporosis as T-score ? -2.5 on DEXA scan (which was obtained post-operatively in our cohort)], who underwent proximal femoral nailing were included. Patients were divided into three groups group 1a-intravenous bisphosphonate ivBP [zoledronic acid (ZA)] given within one week, group 1b-ZA at six weeks and group 2-control group. Post-operative radiographs were assessed for reduction parameters [neck-shaft angle, tip-apex distance, reduction variance]. Radiological union was determined using RUSH score and functional outcome (at one year) with Modified Harris Hip Scores. 41 (23 males), 40 (15 males) and 42 (15 males) patients were included in groups 1a, 1b and 2, respectively (no statistical difference in sex distribution among the groups; p = 0.12). Mean age in groups 1a, 1b and 2 was 71.8 ± 8.1, 75.9 ± 8.5 and 72.3 ± 10.6 years (p = 0.09). There was no significant difference in the pattern of injuries (AO classification) among the groups (p = 0.72). Mean time to union in groups 1a, 1b and 2 was 13.7,13.7 and 14.2 weeks, respectively (p = 0.69). Mean time to union in AO types A1, A2 and A3 fractures was 13.2 ± 2.1, 13.7 ± 2.8 and 16.1 ± 4.9 weeks (p = 0.01). We did not observe any association between T-scores and fracture union (hipp = 0.52, spinep = 0.93).The functional outcome was similar among groups (p = 0.96). Early administration of ZA did not negatively influence fracture healing in patients undergoing fixation of IT fractures. Among the various other factors analyzed, there was a statistically significant association between the fracture type (AO type A3) and longer time to fracture union.Elbow arthrodesis is uncommon and is usually performed as a salvage procedure to provide a stable elbow. There is a significant gap in the literature about the indications, contraindications, fusion angle, technical tips, and reversibility of the procedure. This review addresses these questions in a evidence based manner, based on the published literature.Growing acknowledgement that food systems require transformation, demands comprehensive sustainability assessments that can support decision-making and sustainability governance. To do so, assessment frameworks must be able to make trade-offs and synergies visible and allow for inclusive negotiation on food system outcomes relevant to diverse food system actors. This paper reviews literature and frameworks and builds on stakeholder input to present a Sustainability Compass made up of a comprehensive set of metrics for food system assessments. The Compass defines sustainability scores for four societal goals, underpinned by areas of concern. We demonstrate proof of concept of the operationalization of the approach and its metrics. The Sustainability Compass is able to generate comprehensive food system insights that enables reflexive evaluation and multi-actor negotiation for policy making.The COVID-19 pandemic and related lockdown measures have disrupted food supply chains globally and caused threats to food security, especially in Sub-Saharan Africa. Yet detailed, localized, and timely data on food security threats are rarely available to guide targeted policy interventions. Based on real-time evidence from a pilot project in northern Nigeria, where food insecurity is severe, we illustrate how a digital crowdsourcing platform can provide validated real-time, high frequency, and spatially rich information on the evolution of commodity prices. Daily georeferenced price data of major food commodities were submitted by active volunteer citizens through a mobile phone data collection app and filtered through a stepwise quality control algorithm. We analyzed a total of 23,961 spatially distributed datapoints, contributed by 236 active volunteers, on the price of four commodities (local rice, Thailand rice, white maize and yellow maize) to assess the magnitude of price change over eleven weeks (week 20 to week 30) during and after the first COVID-related lockdown (year 2020), relative to the preceding year (2019). Results show that the retail price of maize (yellow and white) and rice (local and Thailand rice) increased on average by respectively 26% and 44% during this COVID-related period, compared to prices reported in the same period in 2019. GPS-tracked data showed that mobility and market access of active volunteers were reduced, travel-distance to market being 54% less in 2020 compared to 2019, and illustrates potential limitations on consumers who often seek lower pricing by accessing broader markets. Combining the price data with a spatial richness index grid derived from UN-FAO, this study shows the viability of a contactless data crowdsourcing system, backed by an automated quality control process, as a decision-support tool for rapid assessment of price-induced food insecurity risks, and to target interventions (e.g. COVID relief support) at the right time and location(s).In this paper, we establish daily confirmed infected cases prediction models for the time series data of America by applying both the long short-term memory (LSTM) and extreme gradient boosting (XGBoost) algorithms, and employ four performance parameters as MAE, MSE, RMSE, and MAPE to evaluate the effect of model fitting. LSTM is applied to reliably estimate accuracy due to the long-term attribute and diversity of COVID-19 epidemic data. Using XGBoost model, we conduct a sensitivity analysis to determine the robustness of predictive model to parameter features. Our results reveal that achieving a reduction in the contact rate between susceptible and infected individuals by isolated the uninfected individuals, can effectively reduce the number of daily confirmed cases. By combining the restrictive social distancing and contact tracing, the elimination of ongoing COVID-19 pandemic is possible. Our predictions are based on real time series data with reasonable assumptions, whereas the accurate course of epidemic heavily depends on how and when quarantine, isolation and precautionary measures are enforced.